system

Abstract

We provide the system. [Solution] A means of receiving image data, An analysis means for extracting features based on the aforementioned image data, A text generation means that automatically generates text based on the aforementioned features, A data generation means that receives the aforementioned text and generates visual data based on the text, A display means for outputting the generated text and visual data, A system that includes this.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventionally, in a situation where a child exercises their creativity, it has been difficult to give a concrete form to the drawn pictures or created stories, and it has been a problem that parents cannot always assist. In such a case, there has been a need for a tool that can visually and narratively materialize the ideas that children have.

Means for Solving the Problems

[0005] The present invention provides a system that receives image data, extracts features based on it, automatically generates a sentence based on the features, and generates visual data based on the sentence. Thereby, the pictures or stories drawn by children can be shaped into concrete stories or illustrations, and it is possible to support easily materializing the creativity of children.

[0006] "Image data" refers to data that represents visual information in a digital format, and usually includes photographs and illustrations.

[0007] "Communication means" refers to a function or device for sending and receiving data between different devices.

[0008] "Analysis means" refers to a function or device that analyzes input data and extracts useful information or features from it.

[0009] "Feature extraction" is the process of selecting and organizing specific patterns or important data points from input data.

[0010] "Text generation means" refers to a function or device that automatically generates text in natural language based on input data.

[0011] "Natural language processing technology" refers to technologies that enable computers to understand, interpret, and generate human language.

[0012] "Visual data" refers to data that represents visual information, such as images and videos, in digital format.

[0013] "Data generation means" refers to a function or device that creates new data based on input information.

[0014] A "machine learning algorithm" is an algorithm that learns patterns by analyzing data and uses those results to make predictions or generate information.

[0015] "Display means" refers to a function or device for visually outputting processed data. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, let's explain the terminology used in the following explanation.

[0019] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0020] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention provides a system that automatically transforms children's drawings and stories into narratives or illustrations. This system is implemented through a series of operations between a server, a terminal, and a user. A specific example is shown below.

[0038] The user uses their device to digitally capture a drawing they have made and uses this as initial data. The image data of the captured drawing is then sent from the device to the server. For example, suppose the user has drawn a picture of "a cat in a spaceship."

[0039] The server analyzes the image data received from the terminal. This analysis uses machine learning-based image recognition technology, and the server extracts features such as objects and scenes contained within the image. In this case, objects such as "cat" and "spaceship" are extracted.

[0040] Next, the server automatically generates a story using a text generation algorithm based on the extracted features. The server generates content such as "A story about a cat traveling through the galaxy on a spaceship" and organizes this text data.

[0041] The server then uses the generated text data of the story to create visual data. In this process, machine learning algorithms are used to draw illustrations that correspond to the story. For example, an illustration is generated depicting a scene of a cat inside a spaceship flying through outer space.

[0042] Finally, the server sends the generated story and illustrations to the device. Upon receiving them, the device displays the story and illustrations in a format that is easy for the user to view. The user can then appreciate the completed story and illustrations on the device screen and enjoy them as a work of art.

[0043] This invention allows users to automatically generate new stories and illustrations through their own creative activities, thus making it easy to give form to their creativity. This specific embodiment embodies all the elements included in the claims and effectively utilizes the technical features of the present invention.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user captures their own drawings as image data using the device's camera or file selection function. This image data is used as input information for the work.

[0047] Step 2:

[0048] The terminal converts the captured image data into the appropriate format and sends it to the server. Internet-based communication is used for transmission.

[0049] Step 3:

[0050] The server receives image data sent from the terminal. The received images are then passed to the image analysis module.

[0051] Step 4:

[0052] The server uses image analysis technology to extract prominent features and objects from the received image data. For example, features such as "cat" and "spaceship" can be recognized from the image.

[0053] Step 5:

[0054] The server automatically generates a story using a text generation module based on the extracted feature information. Natural language processing techniques are used to generate a grammatically correct story.

[0055] Step 6:

[0056] The server passes the generated story text information to the illustration generation module. Here, visual data is generated to represent the scenes in the story.

[0057] Step 7:

[0058] The server uses machine learning algorithms to generate illustrations related to the story. The generated illustrations will be consistent with the content of the story.

[0059] Step 8:

[0060] The server sends the generated story text and illustrations to the terminal. The transmitted data is then prepared to be displayed on the terminal's screen.

[0061] Step 9:

[0062] The terminal displays the story and illustrations received from the server on the user interface. Users can view these and enjoy the generated works.

[0063] (Example 1)

[0064] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0065] There is a need for a system that can easily generate fun stories and engaging illustrations from children's drawings and original stories, thereby further stimulating creative activities. However, current technology lacks the means to do this automatically and efficiently, forcing users to go through a cumbersome process.

[0066] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0067] In this invention, the server includes communication means for receiving image information, automatic generation means for generating a story based on extracted feature elements, and visualization means for generating visual information based on the story. This makes it possible to automatically generate a story and visual data based on images and support the user's creative activities.

[0068] "Image information" refers to information that represents visual data in a digital format, and is usually provided in image file format.

[0069] "Communication means" refers to network interfaces and protocols for sending and receiving data, enabling data transfer between servers and terminals.

[0070] "Analysis" refers to the process of extracting features from received image information, which involves using image recognition algorithms to break down the data and obtain meaningful information.

[0071] A "feature element" refers to a specific attribute or pattern related to an object or scene, extracted from image information.

[0072] "Automatic generation means" refers to a device that has the function of generating content based on characteristic elements, and in particular, generates stories using natural language generation technology.

[0073] A "story" is a structure that includes a series of events and characters described in writing, and it expresses fictional content.

[0074] A "visualization method" is a device that has the function of generating visual data based on text data such as stories, and uses an automated learning algorithm.

[0075] "Visual information" refers to visual representations such as illustrations and shapes generated based on text data.

[0076] A "display means" is an interface that provides a way to present generated narratives and visual information to the user.

[0077] A "terminal" refers to an electronic device that a user can directly operate and use to send and receive data, and includes smartphones, tablets, and other similar devices.

[0078] This invention provides a system that automatically creates stories or illustrations based on drawings or stories created by children. Specific embodiments of this system are described below.

[0079] Users use their devices to digitally capture their own drawings. This is done using the camera or scanner built into their smartphone or tablet. For example, a user might capture a drawing of "a cat in a spaceship." This image is then saved to the device as a digital image file.

[0080] The device sends the saved image data to the server. A secure communication protocol (e.g., HTTPS) is used for transmission via the internet.

[0081] The server utilizes image recognition technology based on machine learning frameworks (e.g., TENSORFLOW®, PyTorch) to analyze the received image data. As a result of the analysis, feature elements such as "cat" or "spaceship" are extracted from the image.

[0082] Next, the server generates a story using natural language processing techniques (e.g., a natural language generation model) based on these feature elements. Specifically, it provides prompt sentences to the generation AI model. An example of a prompt sentence would be, "Describe an adventure in which a cat travels to a new planet in a spaceship and makes friends there."

[0083] Once the story generation is complete, the server then generates visual information. The server uses machine learning algorithms such as DALL-E and Stable Diffusion as means of generating visual data to automatically create illustrations corresponding to each scene of the story.

[0084] The generated story and illustrations are sent from the server to the terminal. The terminal receives this data and uses an appropriate viewer to display the content in a user-friendly format. Users can appreciate the completed story and illustrations and enjoy them as if they were professional works. This entire process allows users to easily bring their creativity to life.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The user uses a device to capture the image in digital format. Specifically, this involves taking a photograph with a smartphone camera or obtaining an image file using a scanner. In this step, the input is a physical image, and the output is a digital image file (e.g., JPEG, PNG).

[0088] Step 2:

[0089] The terminal sends the acquired image file to the server over the network. A secure communication protocol using the internet (e.g., HTTPS) is used for this process. The input is a digital image file, and the output is the image data securely transferred to the server.

[0090] Step 3:

[0091] The server analyzes the received image data. It applies machine learning models using TensorFlow or PyTorch to extract feature elements (e.g., "cat", "spaceship") from the image. The input is digital image data, and the output is a set of feature elements contained in the image.

[0092] Step 4:

[0093] The server automatically generates a story using natural language processing techniques based on the extracted features. The generating AI model is input with a prompt, such as "Describe an adventure where a cat travels to a new planet in a spaceship and makes friends there." The input consists of feature elements and a prompt, and the output is the text data of the generated story.

[0094] Step 5:

[0095] The server generates visual information (illustrations) based on the text data of the story, using algorithms such as DALL-E and Stable Diffusion. The input is the text data of the story, and the output is the associated illustration data.

[0096] Step 6:

[0097] The server sends the generated story and illustrations to the user's terminal. This transmission process also uses a secure communication protocol. The input is the set of generated story and illustrations, and the output is the data sent to the terminal.

[0098] Step 7:

[0099] The device displays the received story and illustrations. Users can view and enjoy them on the device's display. The input is the story and illustrations received from the server, and the output is the completed work visually presented to the user.

[0100] (Application Example 1)

[0101] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0102] There is a need for a way to easily visualize children's creative activities in narrative and visual form, and to easily share them digitally. Current technology requires manual conversion, which is time-consuming and labor-intensive.

[0103] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0104] In this invention, the server includes receiving means for receiving image information, analysis means for extracting feature information based on the image information, and story generation means for automatically generating a story based on the feature information. This makes it possible to automatically turn a child's drawing into a story, generate visual data based on that story, and quickly provide it as digital content.

[0105] "Image information" refers to visual data expressed in digital format, specifically content represented as pictures or images.

[0106] A "receiving mechanism" is a function that takes in data and information from an external source and converts it into a format that can be used within the system.

[0107] "Feature information" refers to specific elements or attributes extracted from image information, and is used to identify scenes and objects.

[0108] "Analysis means" refers to a device or program that has the function of processing input data and analyzing its components and characteristics.

[0109] A "narrative generation method" is a function for automatically creating text-based stories, constructing content based on extracted feature information.

[0110] "Visual information" refers to visual data generated based on stories or text, and is expressed as illustrations or graphics.

[0111] A "visual generation means" is a device or software for creating visual data based on text information.

[0112] "Display means" refers to a device or program that has the function of showing generated data or information in a form that is recognizable to the user.

[0113] "Organizational methods" refer to the function of combining narrative and visual information and arranging them in the necessary format.

[0114] The system of the present invention includes a terminal used by the user, a server for processing data, and means of communication via the internet. The user uses the terminal to digitize a hand-drawn picture and transmit the image information to the server. The image information is typically acquired using the camera function of a smartphone or tablet.

[0115] The server analyzes the received image information and extracts feature information from it. Image recognition technologies such as Google® Cloud Vision API are used for this analysis. Based on the extracted feature information, the server automatically generates a story using a generative AI model. For example, stories such as "A cat on a spaceship" or "A dog on a pirate ship" are generated.

[0116] Next, the server generates visual information based on the generated story. Models such as DALL-E can be used for this process. The visual information is generated as illustrations corresponding to the story and presented in a user-friendly format.

[0117] The generated stories and visual information are sent to the device, where users can enjoy viewing them. Children, in particular, can experience how their own creative ideas are transformed into digital content.

[0118] As a concrete example, here is an example of a prompt message when a user draws "a dog on a pirate ship".

[0119] Prompt: "The objects drawn are a 'pirate ship' and a 'dog'. Based on these, generate an outline for a children's adventure story."

[0120] This system allows users to easily transform their expressions into digital stories and illustrations and share them with others.

[0121] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0122] Step 1:

[0123] The user uses the device to photograph or scan a hand-drawn picture and saves the image information to the device in digital format. The input is a hand-drawn picture, and the output is digitized image data. This data is prepared for subsequent processing.

[0124] Step 2:

[0125] The terminal transmits the acquired image data to the server. The input is digitized image data, and the output is the transmission of data to the server. The terminal uses a stable communication method to transmit the data.

[0126] Step 3:

[0127] The server analyzes the received image data and extracts feature information using image recognition technology. The input is the transmitted image data, and the output is feature information of objects and scenes within the image. This analysis uses image recognition software (e.g., Google Cloud Vision API).

[0128] Step 4:

[0129] The server uses extracted feature information to generate stories using a generative AI model. The input is feature information, and the output is the text data of the story. Natural language processing techniques are used for story generation, and a story suitable for the content specified by the user is generated.

[0130] Step 5:

[0131] The server generates visual information (illustrations) based on the text data of the story. The input is the text data of the story, and the output is visualized illustration data. In this process, a model (e.g., DALL-E) is used to convert text into visuals.

[0132] Step 6:

[0133] The server combines the generated story and visual information and sends it to the terminal. The input is the story's text data and visual information, and the output is the transmission of data to the terminal. The user receives this data and can view the results on their terminal.

[0134] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0135] This invention relates to a system that recognizes a user's emotions and generates images and text based on those emotions. This system provides a more personalized creative experience by generating stories and illustrations based on drawings created by the user and incorporating the user's emotions.

[0136] Specifically, users use their devices to capture drawings they create and simultaneously send data for emotion recognition to the server. This process includes methods for acquiring data such as pen pressure and speed during drawing, as well as the user's facial expressions.

[0137] The server analyzes the received image data, extracts features, and then recognizes the user's emotions through an emotion engine. For example, the server can perceive "fun" or "creativity" from the color scheme and dynamic elements of a picture. This emotional information then influences the subsequent automated story generation process.

[0138] When generating a story, the server uses the user's emotional data to create text with a more matching theme and tone. For example, if the user's emotion is recognized as "joy," the story will be adjusted to be positive and upbeat.

[0139] Furthermore, emotional information is also used to generate visual data. In addition to the story content, the server can create illustrations that reflect the user's emotions. For example, if the emotion is "calm," an illustration with a generally soft tone will be generated.

[0140] Finally, the server sends the generated story text and illustrations to the device. The device then organizes them and displays a consistent story and visuals to the user.

[0141] Thus, the present invention is a system that takes user emotions into consideration, thereby providing a more personalized experience and enriching user creativity.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] The user uses the device to take a picture of or select a drawing they have created and saves it as image data. At the same time, the device collects emotion-related data such as the user's facial expressions, drawing speed, and pen pressure.

[0145] Step 2:

[0146] The device sends image data and emotion data to the server. A secure and fast communication protocol is used for transmission.

[0147] Step 3:

[0148] The server analyzes the received image data and extracts prominent features and objects from the image. Existing image recognition algorithms are applied to this analysis.

[0149] Step 4:

[0150] The server uses an emotion engine to analyze the transmitted emotion data and recognize the user's current emotional state. For example, emotions such as "joy" or "surprise" may be extracted.

[0151] Step 5:

[0152] The server executes a text generation module based on extracted features and the user's emotional state, automatically generating a story. This story is composed of themes and tones that correspond to the user's emotions.

[0153] Step 6:

[0154] The server creates visual data that reflects the user's emotions based on the generated story. Machine learning algorithms are used to adjust the content and colors.

[0155] Step 7:

[0156] The server sends the generated story and visual data to the terminal. The terminal needs to be able to receive and process the data in real time.

[0157] Step 8:

[0158] The device receives data from the server and displays the story and illustrations in a consistent manner on the user interface. This allows users to enjoy creative works that resonate with their emotions.

[0159] (Example 2)

[0160] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0161] In today's world, users demand personalized content based on their emotions. However, traditional technologies have struggled to accurately recognize user emotions and generate narratives and visual content based on them. As a result, they have been unable to provide creative experiences that align with user intentions and have failed to deliver personalized experiences.

[0162] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0163] In this invention, the server includes communication means for receiving image information, analysis means for extracting features and recognizing emotions based on the image information and user emotion recognition information, text generation means for automatically generating text based on the emotion information, and information generation means for generating visual information based on the generated text and emotion information. This makes it possible to appropriately consider the user's emotions and provide personalized stories and visual content.

[0164] "Image information" refers to drawings and other visual data created by users, and is digital data used by computer systems for analysis and processing.

[0165] "Communication methods" refer to the technologies or protocols used to send or receive data from a terminal to a server, and include the internet and local networks.

[0166] "Emotion recognition information" refers to data used as the basis for determining the user's emotional state, and includes pen pressure, drawing speed, and facial expression data.

[0167] "Features" refer to the prominent properties or patterns of the subject being analyzed, obtained as a result of analyzing image information or emotion recognition information.

[0168] "Analysis means" refers to processing techniques that extract features from received image information and emotion recognition information, and understand the data according to a specific purpose.

[0169] "Emotional information" refers to information that identifies a user's emotional state through analytical means, and this information is used to generate narratives and visual content.

[0170] "Text generation means" refers to technologies or algorithms that perform the process of automatically creating text or stories based on the user's emotional information.

[0171] "Visual information" refers to visual content such as images and illustrations created based on the content of the generated story and the user's emotional information.

[0172] "Information generation means" refers to processes and techniques for generating relevant visual information, taking into account emotional information and generated text.

[0173] "Display means" refers to a method or apparatus for presenting the final text and visual information to the user, and includes output devices such as screens and monitors.

[0174] This system aims to recognize the user's emotions based on drawings and illustrations they create, and then generate new stories and visual content from that. First, the user uses a device to scan or photograph their own drawing, capturing it as a digital image. Along with this image data, information for emotion recognition, such as pen pressure and speed data, and the user's facial expression data, is also acquired. This data is transmitted to a server via a wireless network or internet connection.

[0175] The server uses specialized image processing software to analyze the received image data and extract features. This analysis includes evaluating the image's color scheme, shape, and dynamic elements, and using an emotion engine to recognize emotions. Hue histograms and shape recognition algorithms are often used in this process. For example, emotions such as "joy" or "calmness" can be extracted from the color tones of a painting.

[0176] The server then utilizes a generative AI model to automatically generate a story using the user's emotional information as input data. Using natural language processing techniques, the story is written with themes and styles that match the user's emotions. For example, if the user's emotion is "joy," a bright and positive story will be created. An example of a prompt might be, "Generate a fun adventure story based on a moment when the user felt joy."

[0177] Furthermore, the server generates visual information based on newly created narratives and emotional information. Using machine learning algorithms, it can generate related illustrations and depict scenes expressed in soft colors. The latest technologies in AI models and image editing software are applied to illustration generation.

[0178] Ultimately, the server sends the generated story text and illustrations back to the device. The device then organizes them appropriately and displays visually and textually consistent content to the user. This allows the user to enjoy an emotionally-driven, personalized creative experience.

[0179] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0180] Step 1:

[0181] The user uses a device to scan or photograph their drawing, capturing it as a digital image. This image data is acquired along with emotion recognition information such as pen pressure and drawing speed. The input is the user's drawing and emotion recognition information, and the output is this dataset. Specific software or applications organize the image data and prepare it for transmission to the server.

[0182] Step 2:

[0183] The terminal transmits acquired image data and emotion recognition information to the server. This transmission is securely performed via the Internet Protocol. The input is data on the terminal, and the output is packet data sent to the server. The transmission is performed using the network connection to ensure data integrity.

[0184] Step 3:

[0185] The server analyzes the received image data using dedicated image processing software and extracts features. The input consists of image data and emotion recognition information, and the processing involves analyzing color usage and shape information. The output is feature data of the image. At this stage, a hue histogram and shape recognition algorithm are used to prepare the system for recognizing the user's emotions from the color tone and shape.

[0186] Step 4:

[0187] The server uses an emotion engine to analyze the user's emotions from identified feature data and generate emotion information. The input is feature data, and the output is emotion information. The emotion engine identifies specified patterns, and as a result, emotions such as "joy" or "sadness" are identified.

[0188] Step 5:

[0189] The server uses a generative AI model to automatically generate stories based on emotional information. The input is emotional information, and the output is the text of the story. Through natural language processing technology, it generates text with themes and tones that match the emotions. This process uses the prompt "Generate a fun adventure story based on the scene where the user felt joy."

[0190] Step 6:

[0191] The server uses the generated story text and emotional information to create corresponding visual information using an AI model. The input is the story text and emotional information, and the output is visual data (illustrations). Specifically, machine learning algorithms generate illustrations, visually representing the story's scenes and characters.

[0192] Step 7:

[0193] The server sends the final generated narrative text and visual information to the terminal. The input is the generated data, and the output is the displayed data sent to the terminal. This data is organized for easy user access and transmitted while maintaining visual consistency.

[0194] Step 8:

[0195] The terminal organizes the received narrative text and visual information and displays it to the user. The input is data received from the server, and the output is the story and illustrations presented to the user. This allows the user to enjoy a unique, emotion-based creative experience.

[0196] (Application Example 2)

[0197] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0198] In today's world, where improving the user experience is paramount, there is a demand for personalized content that responds to individual emotions. However, generating illustrations and stories that truly reflect users' emotions is technically difficult, and this has resulted in a limited experience.

[0199] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0200] In this invention, the server includes information acquisition means for receiving image data and emotion data; analysis means for extracting features and recognizing emotions based on the image data and emotion data; and text generation means for automatically generating text based on the features and recognized emotions. This makes it possible to generate personalized text and visual data based on the user's emotions.

[0201] "Image data" refers to digital data that records visual information and is used in analysis and generation processes.

[0202] "Emotional data" refers to data that indicates a user's emotional state, and is information obtained based on the user's facial expressions and behavioral characteristics.

[0203] "Information acquisition means" refers to a function or mechanism for receiving image data and emotional data via a digital device or system.

[0204] "Analysis means" refers to a device or program that uses a process or technology to extract features from received data and recognize emotions.

[0205] "Text generation means" refers to a device or program that uses a process or technology to automatically generate text based on analyzed feature and sentiment data.

[0206] "Data generation means" refers to a device or program that uses a process or technology to create visual data based on generated text and emotional information.

[0207] "Display means" refers to an output device or technology used to provide the generated text and visual data to the user.

[0208] The system for implementing this invention consists of a terminal used by the user and a server in the cloud. First, the user imports image data into the terminal via a smartphone or smart glasses. This allows for the collection of data based on the user's photographs and changes in their emotions. For example, the system extracts the user's facial expression data from a photograph of their face.

[0209] The server receives image data and emotion data using information acquisition means. Here, cloud services such as AWS® and Azure® are used for data reception and processing. The received data is analyzed by analysis means using image analysis libraries (e.g., OpenCV) to recognize the user's emotional state. Microsoft® Azure Emotion API is used for emotion recognition.

[0210] Next, the text generation means automatically generates text using a generative AI model (e.g., OpenAI®'s GPT) based on the analyzed features and the user's emotions. In the text generation process, prompt sentences are provided, and the generative AI model creates text that matches the emotions.

[0211] Subsequently, the server uses data generation tools to apply machine learning algorithms and sentiment evaluation models to generate visual data based on the generated text and recognized emotions. Generative AI such as DALLE-2 is used to generate the visual data, creating illustrations that align with the user's emotions.

[0212] The generated content is returned to the device via a display device and provided to the user. This allows the user to experience personalized text and art based on their own emotions.

[0213] As a concrete example, when a user takes a photo of autumn leaves, the photo data is input into the system, and the user's smiling expression data is obtained. If the emotion is recognized as "happiness," the following prompt message is input into the AI ​​model to generate a story and art.

[0214] Example of a prompt:

[0215] "The user's photo features an autumn background with a predominantly red and orange color scheme. The user is smiling and appears calm and happy. Please write a story themed around a happy autumn."

[0216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0217] Step 1:

[0218] The user takes image data using a smartphone and imports it to the device. The input is the captured image data, and the output is a digital image file stored on the device. This file also includes metadata such as the user's current location, time, and shooting conditions.

[0219] Step 2:

[0220] The terminal sends image data it has captured to the server. The server receives the image data using an information acquisition method. The input is image data from the terminal, and the output is the image file transferred to the server. HTTPS is used as the communication protocol.

[0221] Step 3:

[0222] The server analyzes image data and extracts features using an image analysis library (e.g., OpenCV). The input is the received image data, and the output is analyzed feature data such as color and shape. For example, it can identify the main colors in an image and extract them as a data set.

[0223] Step 4:

[0224] The server uses the Microsoft Azure Emotion API to recognize emotions. The input consists of features extracted from image data and data indicating the user's facial expressions, while the output is data indicating the user's emotional state. This data is labeled, for example, with terms like "joy" or "sadness."

[0225] Step 5:

[0226] The server generates text using a generative AI model (e.g., OpenAI's GPT). The input is emotion data and associated prompt sentences, and the output is the generated text data. The prompt sentences are in the form of "Please write a story about when the user's emotion is XX," and the generative AI model generates an appropriate story.

[0227] Step 6:

[0228] The server generates visual data using data generation methods. The input is generated text and emotion data, and the output is illustration data that corresponds to the emotion. This is a process that visualizes images within text using a generative AI model (e.g., DALLE-2).

[0229] Step 7:

[0230] The server sends generated text and visual data to the terminal. The input is the text and visual data generated on the server, and the output is the content sent to the terminal. This allows the user to see stories and art that resonate with their emotions.

[0231] Step 8:

[0232] The device provides the user with the data it receives through a display mechanism. The input consists of text and visual data received from the server, while the output is customized content displayed on the user's screen. This allows the user to enjoy a personalized experience based on their own emotions.

[0233] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0234] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0235] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0236] [Second Embodiment]

[0237] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0238] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0239] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0240] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0241] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0242] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0243] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0244] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0245] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0246] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0247] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0248] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0249] This invention provides a system that automatically transforms children's drawings and stories into narratives or illustrations. This system is implemented through a series of operations between a server, a terminal, and a user. A specific example is shown below.

[0250] The user uses their device to digitally capture a drawing they have made and uses this as initial data. The image data of the captured drawing is then sent from the device to the server. For example, suppose the user has drawn a picture of "a cat in a spaceship."

[0251] The server analyzes the image data received from the terminal. This analysis uses machine learning-based image recognition technology, and the server extracts features such as objects and scenes contained within the image. In this case, objects such as "cat" and "spaceship" are extracted.

[0252] Next, the server automatically generates a story using a text generation algorithm based on the extracted features. The server generates content such as "A story about a cat traveling through the galaxy on a spaceship" and organizes this text data.

[0253] The server then uses the generated text data of the story to create visual data. In this process, machine learning algorithms are used to draw illustrations that correspond to the story. For example, an illustration is generated depicting a scene of a cat inside a spaceship flying through outer space.

[0254] Finally, the server sends the generated story and illustrations to the device. Upon receiving them, the device displays the story and illustrations in a format that is easy for the user to view. The user can then appreciate the completed story and illustrations on the device screen and enjoy them as a work of art.

[0255] This invention allows users to automatically generate new stories and illustrations through their own creative activities, thus making it easy to give form to their creativity. This specific embodiment embodies all the elements included in the claims and effectively utilizes the technical features of the present invention.

[0256] The following describes the processing flow.

[0257] Step 1:

[0258] The user captures their own drawings as image data using the device's camera or file selection function. This image data is used as input information for the work.

[0259] Step 2:

[0260] The terminal converts the captured image data into the appropriate format and sends it to the server. Internet-based communication is used for transmission.

[0261] Step 3:

[0262] The server receives image data sent from the terminal. The received images are then passed to the image analysis module.

[0263] Step 4:

[0264] The server uses image analysis technology to extract prominent features and objects from the received image data. For example, features such as "cat" and "spaceship" can be recognized from the image.

[0265] Step 5:

[0266] The server automatically generates a story using a text generation module based on the extracted feature information. Natural language processing techniques are used to generate a grammatically correct story.

[0267] Step 6:

[0268] The server passes the generated story text information to the illustration generation module. Here, visual data is generated to represent the scenes in the story.

[0269] Step 7:

[0270] The server uses machine learning algorithms to generate illustrations related to the story. The generated illustrations will be consistent with the content of the story.

[0271] Step 8:

[0272] The server sends the generated story text and illustrations to the terminal. The transmitted data is then prepared to be displayed on the terminal's screen.

[0273] Step 9:

[0274] The terminal displays the story and illustrations received from the server on the user interface. Users can view these and enjoy the generated works.

[0275] (Example 1)

[0276] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0277] There is a need for a system that can easily generate fun stories and engaging illustrations from children's drawings and original stories, thereby further stimulating creative activities. However, current technology lacks the means to do this automatically and efficiently, forcing users to go through a cumbersome process.

[0278] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0279] In this invention, the server includes communication means for receiving image information, automatic generation means for generating a story based on extracted feature elements, and visualization means for generating visual information based on the story. This makes it possible to automatically generate a story and visual data based on images and support the user's creative activities.

[0280] "Image information" refers to information that represents visual data in a digital format, and is usually provided in image file format.

[0281] "Communication means" refers to network interfaces and protocols for sending and receiving data, enabling data transfer between servers and terminals.

[0282] "Analysis" refers to the process of extracting features from the received image information, which means decomposing data using an image recognition algorithm to obtain meaningful information.

[0283] "Feature elements" refer to those that represent specific attributes or patterns related to objects or scenes, extracted from image information.

[0284] "Automatic generation means" refers to something that has the function of generating content based on feature elements, especially generating a story using natural language generation technology.

[0285] "Story" refers to a composition that includes a series of events and characters described by text, representing the created content.

[0286] "Visualization means" refers to something that has the function of generating visual data based on text data such as a story, and uses an automatic learning algorithm.

[0287] "Visual information" refers to visual expressions such as illustrations and graphics generated based on text data.

[0288] "Display means" refers to something that has an interface for presenting the generated story and visual information to the user.

[0289] "Terminal" refers to an electronic device that can be directly operated by a user and can send and receive data, including smartphones, tablets, etc.

[0290] This invention provides a system that automatically creates a story or an illustration based on a picture drawn or a story created by a child. The specific embodiments of this system will be described below.

[0291] Users use their devices to digitally capture their own drawings. This is done using the camera or scanner built into their smartphone or tablet. For example, a user might capture a drawing of "a cat in a spaceship." This image is then saved to the device as a digital image file.

[0292] The device sends the saved image data to the server. A secure communication protocol (e.g., HTTPS) is used for transmission via the internet.

[0293] The server utilizes image recognition technology based on machine learning frameworks (e.g., TensorFlow, PyTorch) to analyze the received image data. As a result of the analysis, feature elements such as "cat" or "spaceship" are extracted from the image.

[0294] Next, the server generates a story using natural language processing techniques (e.g., a natural language generation model) based on these feature elements. Specifically, it provides prompt sentences to the generation AI model. An example of a prompt sentence would be, "Describe an adventure in which a cat travels to a new planet in a spaceship and makes friends there."

[0295] Once the story generation is complete, the server then generates visual information. The server uses machine learning algorithms such as DALL-E and Stable Diffusion as means of generating visual data to automatically create illustrations corresponding to each scene of the story.

[0296] The generated story and illustrations are sent from the server to the terminal. The terminal receives this data and uses an appropriate viewer to display the content in a user-friendly format. Users can appreciate the completed story and illustrations and enjoy them as if they were professional works. This entire process allows users to easily bring their creativity to life.

[0297] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0298] Step 1:

[0299] The user uses a device to capture the image in digital format. Specifically, this involves taking a photograph with a smartphone camera or obtaining an image file using a scanner. In this step, the input is a physical image, and the output is a digital image file (e.g., JPEG, PNG).

[0300] Step 2:

[0301] The terminal sends the acquired image file to the server over the network. A secure communication protocol using the internet (e.g., HTTPS) is used for this process. The input is a digital image file, and the output is the image data securely transferred to the server.

[0302] Step 3:

[0303] The server analyzes the received image data. It applies machine learning models using TensorFlow or PyTorch to extract feature elements (e.g., "cat," "spaceship") from the image. The input is digital image data, and the output is a set of feature elements contained in the image.

[0304] Step 4:

[0305] The server automatically generates a story using natural language processing techniques based on the extracted features. The generating AI model is input with a prompt, such as "Describe an adventure where a cat travels to a new planet in a spaceship and makes friends there." The input consists of feature elements and a prompt, and the output is the text data of the generated story.

[0306] Step 5:

[0307] Based on the text data of the story, the server uses algorithms such as DALL-E and Stable Diffusion to generate visual information (illustrations). The input is the text data of the story, and the output is the related illustration data.

[0308] Step 6:

[0309] The server sends the generated story and illustrations to the user's terminal. This transmission process also uses a secure communication protocol. The input is the set of the generated story and illustrations, and the output is the data sent to the terminal.

[0310] Step 7:

[0311] The terminal displays the received story and illustrations. The user can view and enjoy them on the terminal display. The input is the story and illustrations received from the server, and the output is the completed work visually presented to the user.

[0312] (Application Example 1)

[0313] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0314] <000—0989>There is a need for a means to easily visualize the creative activities expressed by children in the form of stories and visuals and easily share them in digital form. The current technology has the problem that manual conversion is required, which is time-consuming and laborious.

[0315] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0316] In this invention, the server includes receiving means for receiving image information, analysis means for extracting feature information based on the image information, and story generation means for automatically generating a story based on the feature information. This makes it possible to automatically turn a child's drawing into a story, generate visual data based on that story, and quickly provide it as digital content.

[0317] "Image information" refers to visual data expressed in digital format, specifically content represented as pictures or images.

[0318] A "receiving mechanism" is a function that takes in data and information from an external source and converts it into a format that can be used within the system.

[0319] "Feature information" refers to specific elements or attributes extracted from image information, and is used to identify scenes and objects.

[0320] "Analysis means" refers to a device or program that has the function of processing input data and analyzing its components and characteristics.

[0321] A "narrative generation method" is a function for automatically creating text-based stories, constructing content based on extracted feature information.

[0322] "Visual information" refers to visual data generated based on stories or text, and is expressed as illustrations or graphics.

[0323] A "visual generation means" is a device or software for creating visual data based on text information.

[0324] "Display means" refers to a device or program that has the function of showing generated data or information in a form that is recognizable to the user.

[0325] "Organizational methods" refer to the function of combining narrative and visual information and arranging them in the necessary format.

[0326] The system of the present invention includes a terminal used by the user, a server for processing data, and means of communication via the internet. The user uses the terminal to digitize a hand-drawn picture and transmit the image information to the server. The image information is typically acquired using the camera function of a smartphone or tablet.

[0327] The server analyzes the received image information and extracts feature information from it. Image recognition technologies such as the Google Cloud Vision API are used for this analysis. Based on the extracted feature information, the server automatically generates stories using a generative AI model. For example, stories such as "A cat on a spaceship" or "A dog on a pirate ship" are generated.

[0328] Next, the server generates visual information based on the generated story. Models such as DALL-E can be used for this process. The visual information is generated as illustrations corresponding to the story and presented in a user-friendly format.

[0329] The generated stories and visual information are sent to the device, where users can enjoy viewing them. Children, in particular, can experience how their own creative ideas are transformed into digital content.

[0330] As a concrete example, here is an example of a prompt message when a user draws "a dog on a pirate ship".

[0331] Prompt: "The objects drawn are a 'pirate ship' and a 'dog'. Based on these, generate an outline for a children's adventure story."

[0332] This system allows users to easily transform their expressions into digital stories and illustrations and share them with others.

[0333] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0334] Step 1:

[0335] The user uses the device to photograph or scan a hand-drawn picture and saves the image information to the device in digital format. The input is a hand-drawn picture, and the output is digitized image data. This data is prepared for subsequent processing.

[0336] Step 2:

[0337] The terminal transmits the acquired image data to the server. The input is digitized image data, and the output is the transmission of data to the server. The terminal uses a stable communication method to transmit the data.

[0338] Step 3:

[0339] The server analyzes the received image data and extracts feature information using image recognition technology. The input is the transmitted image data, and the output is feature information of objects and scenes within the image. This analysis uses image recognition software (e.g., Google Cloud Vision API).

[0340] Step 4:

[0341] The server uses extracted feature information to generate stories using a generative AI model. The input is feature information, and the output is the text data of the story. Natural language processing techniques are used for story generation, and a story suitable for the content specified by the user is generated.

[0342] Step 5:

[0343] The server generates visual information (illustrations) based on the text data of the story. The input is the text data of the story, and the output is visualized illustration data. In this process, a model (e.g., DALL-E) is used to convert text into visuals.

[0344] Step 6:

[0345] The server combines the generated story and visual information and sends it to the terminal. The input is the story's text data and visual information, and the output is the transmission of data to the terminal. The user receives this data and can view the results on their terminal.

[0346] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0347] This invention relates to a system that recognizes a user's emotions and generates images and text based on those emotions. This system provides a more personalized creative experience by generating stories and illustrations based on drawings created by the user and incorporating the user's emotions.

[0348] Specifically, the user uses their device to capture a drawing they have created, simultaneously sending data for emotion recognition to the server. This process includes methods for acquiring data such as pen pressure and speed during drawing, as well as the user's facial expressions.

[0349] The server analyzes the received image data, extracts features, and then recognizes the user's emotions through an emotion engine. For example, the server can perceive "fun" or "creativity" from the color scheme and dynamic elements of a picture. This emotional information then influences the subsequent automated story generation process.

[0350] When generating a story, the server uses the user's emotional data to create text with a more matching theme and tone. For example, if the user's emotion is recognized as "joy," the story will be adjusted to be positive and upbeat.

[0351] Furthermore, emotional information is also used to generate visual data. In addition to the story content, the server can create illustrations that reflect the user's emotions. For example, if the emotion is "calm," an illustration with a generally soft tone will be generated.

[0352] Finally, the server sends the generated story text and illustrations to the device. The device then organizes them and displays a consistent story and visuals to the user.

[0353] Thus, the present invention is a system that takes user emotions into consideration, thereby providing a more personalized experience and enriching user creativity.

[0354] The following describes the processing flow.

[0355] Step 1:

[0356] The user uses the device to take a picture of or select a drawing they have created and saves it as image data. At the same time, the device collects emotion-related data such as the user's facial expressions, drawing speed, and pen pressure.

[0357] Step 2:

[0358] The device sends image data and emotion data to the server. A secure and fast communication protocol is used for transmission.

[0359] Step 3:

[0360] The server analyzes the received image data and extracts prominent features and objects from the image. Existing image recognition algorithms are applied to this analysis.

[0361] Step 4:

[0362] The server uses an emotion engine to analyze the transmitted emotion data and recognize the user's current emotional state. For example, emotions such as "joy" or "surprise" may be extracted.

[0363] Step 5:

[0364] The server executes a text generation module based on extracted features and the user's emotional state, automatically generating a story. This story is composed of themes and tones that correspond to the user's emotions.

[0365] Step 6:

[0366] The server creates visual data that reflects the user's emotions based on the generated story. Machine learning algorithms are used to adjust the content and colors.

[0367] Step 7:

[0368] The server sends the generated story and visual data to the terminal. The terminal needs to be able to receive and process the data in real time.

[0369] Step 8:

[0370] The device receives data from the server and displays the story and illustrations in a consistent manner on the user interface. This allows users to enjoy creative works that resonate with their emotions.

[0371] (Example 2)

[0372] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0373] In today's world, users demand personalized content based on their emotions. However, traditional technologies have struggled to accurately recognize user emotions and generate narratives and visual content based on them. As a result, they have been unable to provide creative experiences that align with user intentions and have failed to deliver personalized experiences.

[0374] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0375] In this invention, the server includes communication means for receiving image information, analysis means for extracting features and recognizing emotions based on the image information and user emotion recognition information, text generation means for automatically generating text based on the emotion information, and information generation means for generating visual information based on the generated text and emotion information. This makes it possible to appropriately consider the user's emotions and provide personalized stories and visual content.

[0376] "Image information" refers to drawings and other visual data created by users, and is digital data used by computer systems for analysis and processing.

[0377] "Communication methods" refer to the technologies or protocols used to send or receive data from a terminal to a server, and include the internet and local networks.

[0378] "Emotion recognition information" refers to data used as the basis for determining the user's emotional state, and includes pen pressure, drawing speed, and facial expression data.

[0379] "Features" refer to the prominent properties or patterns of the subject being analyzed, obtained as a result of analyzing image information or emotion recognition information.

[0380] "Analysis means" refers to processing techniques that extract features from received image information and emotion recognition information, and understand the data according to a specific purpose.

[0381] "Emotional information" refers to information that identifies a user's emotional state through analytical means, and this information is used to generate narratives and visual content.

[0382] "Text generation means" refers to a technology or algorithm that performs a process of automatically creating text or stories based on the user's emotional information.

[0383] "Visual information" refers to visual content such as images and illustrations created based on the content of the generated story and the user's emotional information.

[0384] "Information generation means" refers to processes and techniques for generating relevant visual information, taking into account emotional information and generated text.

[0385] "Display means" refers to a method or apparatus for presenting the final text and visual information to the user, and includes output devices such as screens and monitors.

[0386] This system aims to recognize the user's emotions based on drawings and illustrations they create, and then generate new stories and visual content from that. First, the user uses a device to scan or photograph their own drawing, capturing it as a digital image. Along with this image data, information for emotion recognition, such as pen pressure and speed data, and the user's facial expression data, is also acquired. This data is transmitted to a server via a wireless network or internet connection.

[0387] The server uses specialized image processing software to analyze the received image data and extract features. This analysis includes evaluating the image's color scheme, shape, and dynamic elements, and using an emotion engine to recognize emotions. Hue histograms and shape recognition algorithms are often used in this process. For example, emotions such as "joy" or "calmness" can be extracted from the color tones of a painting.

[0388] The server then utilizes a generative AI model to automatically generate a story using the user's emotional information as input data. Using natural language processing techniques, the story is created with themes and styles that match the user's emotions. For example, if the user's emotion is "joy," a bright and positive story will be constructed. An example of a prompt might be, "Generate a fun adventure story based on a moment when the user felt joy."

[0389] Furthermore, the server generates visual information based on newly created narratives and emotional information. Using machine learning algorithms, it can generate related illustrations and depict scenes expressed in soft colors. The latest technologies in AI models and image editing software are applied to illustration generation.

[0390] Ultimately, the server sends the generated story text and illustrations back to the device. The device then organizes them appropriately and displays visually and textually consistent content to the user. This allows the user to enjoy an emotionally-driven, personalized creative experience.

[0391] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0392] Step 1:

[0393] The user uses a device to scan or photograph their drawing, capturing it as a digital image. This image data is acquired along with emotion recognition information such as pen pressure and drawing speed. The input is the user's drawing and emotion recognition information, and the output is this dataset. Specific software or applications organize the image data and prepare it for transmission to the server.

[0394] Step 2:

[0395] The terminal transmits acquired image data and emotion recognition information to the server. This transmission is securely performed via the Internet Protocol. The input is data on the terminal, and the output is packet data sent to the server. The transmission is performed using the network connection to ensure data integrity.

[0396] Step 3:

[0397] The server analyzes the received image data using dedicated image processing software and extracts features. The input consists of image data and emotion recognition information, and the processing involves analyzing color usage and shape information. The output is feature data of the image. At this stage, a hue histogram and shape recognition algorithm are used to prepare the system for recognizing the user's emotions from the color tone and shape.

[0398] Step 4:

[0399] The server uses an emotion engine to analyze the user's emotions from identified feature data and generate emotion information. The input is feature data, and the output is emotion information. The emotion engine identifies specified patterns, and as a result, emotions such as "joy" or "sadness" are identified.

[0400] Step 5:

[0401] The server uses a generative AI model to automatically generate stories based on emotional information. The input is emotional information, and the output is the text of the story. Through natural language processing technology, it generates text with themes and tones that match the emotions. This process uses the prompt "Generate a fun adventure story based on the scene where the user felt joy."

[0402] Step 6:

[0403] The server uses the generated story text and emotional information to create corresponding visual information using an AI model. The input is the story text and emotional information, and the output is visual data (illustrations). Specifically, machine learning algorithms generate illustrations, visually representing the story's scenes and characters.

[0404] Step 7:

[0405] The server sends the final generated narrative text and visual information to the terminal. The input is the generated data, and the output is the displayed data sent to the terminal. This data is organized for easy user access and transmitted while maintaining visual consistency.

[0406] Step 8:

[0407] The terminal organizes the received narrative text and visual information and displays it to the user. The input is data received from the server, and the output is the story and illustrations presented to the user. This allows the user to enjoy a unique, emotion-based creative experience.

[0408] (Application Example 2)

[0409] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0410] In today's world, where improving user experience is paramount, there is a growing demand for personalized content that responds to individual emotions. However, generating illustrations and stories that truly reflect user emotions is technically challenging, and this has resulted in a limited user experience.

[0411] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0412] In this invention, the server includes information acquisition means for receiving image data and emotion data; analysis means for extracting features and recognizing emotions based on the image data and emotion data; and text generation means for automatically generating text based on the features and recognized emotions. This enables the generation of personalized text and visual data based on the user's emotions.

[0413] "Image data" refers to digital data that records visual information and is used in analysis and generation processes.

[0414] "Emotional data" refers to data that indicates a user's emotional state, and is information obtained based on the user's facial expressions and behavioral characteristics.

[0415] "Information acquisition means" refers to a function or mechanism for receiving image data and emotional data via a digital device or system.

[0416] "Analysis means" refers to a device or program that uses a process or technology to extract features from received data and recognize emotions.

[0417] "Text generation means" refers to a device or program that uses a process or technology to automatically generate text based on analyzed feature and sentiment data.

[0418] "Data generation means" refers to a device or program that uses a process or technology to create visual data based on generated text and emotional information.

[0419] "Display means" refers to an output device or technology used to provide the generated text and visual data to the user.

[0420] The system for implementing this invention consists of a terminal used by the user and a server in the cloud. First, the user imports image data into the terminal via a smartphone or smart glasses. This allows for the collection of data based on the user's photographs and changes in their emotions. For example, the system extracts the user's facial expression data from a photograph of their face.

[0421] The server receives image data and emotion data using information acquisition means. Here, cloud services such as AWS and Azure are used for data reception and processing. The received data is analyzed by analysis means using image analysis libraries (e.g., OpenCV) to recognize the user's emotional state. Microsoft Azure Emotion API is used for emotion recognition.

[0422] Next, the text generation means automatically generates text using a generative AI model (e.g., OpenAI's GPT) based on the analyzed features and the user's emotions. In the text generation process, prompt sentences are provided, and the generative AI model creates text that matches the emotions.

[0423] Subsequently, the server uses data generation tools to apply machine learning algorithms and sentiment evaluation models to generate visual data based on the generated text and recognized emotions. Generative AI such as DALLE-2 is used to generate the visual data, creating illustrations that align with the user's emotions.

[0424] The generated content is returned to the device via a display device and provided to the user. This allows the user to experience personalized text and art based on their own emotions.

[0425] As a concrete example, when a user takes a photo of autumn leaves, the photo data is input into the system, and the user's smiling expression data is obtained. If the emotion is recognized as "happiness," the following prompt message is input into the AI ​​model to generate a story and art.

[0426] Example of a prompt:

[0427] "The user's photo features an autumn background with a predominantly red and orange color scheme. The user is smiling and appears calm and happy. Please write a story themed around a happy autumn."

[0428] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0429] Step 1:

[0430] The user takes image data using a smartphone and imports it to the device. The input is the captured image data, and the output is a digital image file stored on the device. This file also includes metadata such as the user's current location, time, and shooting conditions.

[0431] Step 2:

[0432] The terminal sends image data it has captured to the server. The server receives the image data using an information acquisition method. The input is image data from the terminal, and the output is the image file transferred to the server. HTTPS is used as the communication protocol.

[0433] Step 3:

[0434] The server analyzes image data and extracts features using an image analysis library (e.g., OpenCV). The input is the received image data, and the output is analyzed feature data such as color and shape. For example, it can identify the main colors in an image and extract them as a data set.

[0435] Step 4:

[0436] The server uses the Microsoft Azure Emotion API to recognize emotions. The input consists of features extracted from image data and data representing the user's facial expressions, while the output is data representing the user's emotional state. This data is labeled with terms such as "joy" or "sadness."

[0437] Step 5:

[0438] The server generates text using a generative AI model (e.g., OpenAI's GPT). The input is emotion data and associated prompt sentences, and the output is the generated text data. The prompt sentences are in the form of "Please write a story about when the user's emotion is XX," and the generative AI model generates an appropriate story.

[0439] Step 6:

[0440] The server generates visual data using data generation methods. The input is generated text and emotion data, and the output is illustration data that corresponds to the emotion. This is a process that visualizes images within text using a generative AI model (e.g., DALLE-2).

[0441] Step 7:

[0442] The server sends generated text and visual data to the terminal. The input is the text and visual data generated on the server, and the output is the content sent to the terminal. This allows the user to see stories and art that resonate with their emotions.

[0443] Step 8:

[0444] The device provides the user with the data it receives through a display mechanism. The input consists of text and visual data received from the server, while the output is customized content displayed on the user's screen. This allows the user to enjoy a personalized experience based on their own emotions.

[0445] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0446] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0447] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0448] [Third Embodiment]

[0449] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0450] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0451] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0452] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0453] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0454] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0455] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0456] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0457] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0458] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0459] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0460] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0461] This invention provides a system that automatically transforms children's drawings and stories into narratives or illustrations. This system is implemented through a series of operations between a server, a terminal, and a user. A specific example is shown below.

[0462] The user uses their device to digitally capture a drawing they have made and uses this as initial data. The image data of the captured drawing is then sent from the device to the server. For example, suppose the user has drawn a picture of "a cat in a spaceship."

[0463] The server analyzes the image data received from the terminal. This analysis uses machine learning-based image recognition technology, and the server extracts features such as objects and scenes contained within the image. In this case, objects such as "cat" and "spaceship" are extracted.

[0464] Next, the server automatically generates a story using a text generation algorithm based on the extracted features. The server generates content such as "A story about a cat traveling through the galaxy on a spaceship" and organizes this text data.

[0465] The server then uses the generated text data of the story to create visual data. In this process, machine learning algorithms are used to draw illustrations that correspond to the story. For example, an illustration is generated depicting a scene of a cat inside a spaceship flying through outer space.

[0466] Finally, the server sends the generated story and illustrations to the device. Upon receiving them, the device displays the story and illustrations in a format that is easy for the user to view. The user can then appreciate the completed story and illustrations on the device screen and enjoy them as a work of art.

[0467] This invention allows users to automatically generate new stories and illustrations through their own creative activities, thus making it easy to give form to their creativity. This specific embodiment embodies all the elements included in the claims and effectively utilizes the technical features of the present invention.

[0468] The following describes the processing flow.

[0469] Step 1:

[0470] The user captures their own drawings as image data using the device's camera or file selection function. This image data is used as input information for the work.

[0471] Step 2:

[0472] The terminal converts the captured image data into the appropriate format and sends it to the server. Internet-based communication is used for transmission.

[0473] Step 3:

[0474] The server receives image data sent from the terminal. The received images are then passed to the image analysis module.

[0475] Step 4:

[0476] The server uses image analysis technology to extract prominent features and objects from the received image data. For example, features such as "cat" and "spaceship" can be recognized from the image.

[0477] Step 5:

[0478] The server automatically generates a story using a text generation module based on the extracted feature information. Natural language processing techniques are used to generate a grammatically correct story.

[0479] Step 6:

[0480] The server passes the generated story text information to the illustration generation module. Here, visual data is generated to represent the scenes in the story.

[0481] Step 7:

[0482] The server uses machine learning algorithms to generate illustrations related to the story. The generated illustrations will be consistent with the content of the story.

[0483] Step 8:

[0484] The server sends the generated story text and illustrations to the terminal. The transmitted data is then prepared to be displayed on the terminal's screen.

[0485] Step 9:

[0486] The terminal displays the story and illustrations received from the server on the user interface. Users can view these and enjoy the generated works.

[0487] (Example 1)

[0488] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0489] There is a need for a system that can easily generate fun stories and engaging illustrations from children's drawings and original stories, thereby further stimulating creative activities. However, current technology lacks the means to do this automatically and efficiently, forcing users to go through a cumbersome process.

[0490] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0491] In this invention, the server includes communication means for receiving image information, automatic generation means for generating a story based on extracted feature elements, and visualization means for generating visual information based on the story. This makes it possible to automatically generate a story and visual data based on images and support the user's creative activities.

[0492] "Image information" refers to information that represents visual data in a digital format, and is usually provided in image file format.

[0493] "Communication means" refers to network interfaces and protocols for sending and receiving data, enabling data transfer between servers and terminals.

[0494] "Analysis" refers to the process of extracting features from received image information, which involves using image recognition algorithms to break down the data and obtain meaningful information.

[0495] A "feature element" refers to a specific attribute or pattern related to an object or scene, extracted from image information.

[0496] "Automatic generation means" refers to a device that has the function of generating content based on characteristic elements, and in particular, generates stories using natural language generation technology.

[0497] A "story" is a structure that includes a series of events and characters described in writing, and it expresses fictional content.

[0498] A "visualization method" is a device that has the function of generating visual data based on text data such as stories, and uses an automated learning algorithm.

[0499] "Visual information" refers to visual representations such as illustrations and shapes generated based on text data.

[0500] A "display means" is an interface that provides a way to present generated narratives and visual information to the user.

[0501] A "terminal" refers to an electronic device that a user can directly operate and use to send and receive data, and includes smartphones, tablets, and other similar devices.

[0502] This invention provides a system that automatically creates stories or illustrations based on drawings or stories created by children. Specific embodiments of this system are described below.

[0503] Users use their devices to digitally capture their own drawings. This is done using the camera or scanner built into their smartphone or tablet. For example, a user might capture a drawing of "a cat in a spaceship." This image is then saved to the device as a digital image file.

[0504] The device sends the saved image data to the server. A secure communication protocol (e.g., HTTPS) is used for transmission via the internet.

[0505] The server utilizes image recognition technology based on machine learning frameworks (e.g., TensorFlow, PyTorch) to analyze the received image data. As a result of the analysis, feature elements such as "cat" or "spaceship" are extracted from the image.

[0506] Next, the server generates a story using natural language processing techniques (e.g., a natural language generation model) based on these feature elements. Specifically, it provides prompt sentences to the generation AI model. An example of a prompt sentence would be, "Describe an adventure in which a cat travels to a new planet in a spaceship and makes friends there."

[0507] Once the story generation is complete, the server then generates visual information. The server uses machine learning algorithms such as DALL-E and Stable Diffusion as means of generating visual data to automatically create illustrations corresponding to each scene of the story.

[0508] The generated story and illustrations are sent from the server to the terminal. The terminal receives this data and uses an appropriate viewer to display the content in a user-friendly format. Users can appreciate the completed story and illustrations and enjoy them as if they were professional works. This entire process allows users to easily bring their creativity to life.

[0509] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0510] Step 1:

[0511] The user uses a device to capture the image in digital format. Specifically, this involves taking a photograph with a smartphone camera or obtaining an image file using a scanner. In this step, the input is a physical image, and the output is a digital image file (e.g., JPEG, PNG).

[0512] Step 2:

[0513] The terminal sends the acquired image file to the server over the network. A secure communication protocol using the internet (e.g., HTTPS) is used for this process. The input is a digital image file, and the output is the image data securely transferred to the server.

[0514] Step 3:

[0515] The server analyzes the received image data. It applies machine learning models using TensorFlow or PyTorch to extract feature elements (e.g., "cat," "spaceship") from the image. The input is digital image data, and the output is a set of feature elements contained in the image.

[0516] Step 4:

[0517] The server automatically generates a story using natural language processing techniques based on the extracted features. The generating AI model is input with a prompt, such as "Describe an adventure where a cat travels to a new planet in a spaceship and makes friends there." The input consists of feature elements and a prompt, and the output is the text data of the generated story.

[0518] Step 5:

[0519] The server generates visual information (illustrations) based on the story's text data, using algorithms such as DALL-E and Stable Diffusion. The input is the story's text data, and the output is the associated illustration data.

[0520] Step 6:

[0521] The server sends the generated story and illustrations to the user's terminal. This transmission process also uses a secure communication protocol. The input is the set of generated story and illustrations, and the output is the data sent to the terminal.

[0522] Step 7:

[0523] The device displays the received story and illustrations. Users can view and enjoy them on the device's display. The input is the story and illustrations received from the server, and the output is the completed work visually presented to the user.

[0524] (Application Example 1)

[0525] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0526] There is a need for a way to easily visualize children's creative activities in narrative and visual form, and to easily share them digitally. Current technology requires manual conversion, which is time-consuming and labor-intensive.

[0527] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0528] In this invention, the server includes receiving means for receiving image information, analysis means for extracting feature information based on the image information, and story generation means for automatically generating a story based on the feature information. This makes it possible to automatically turn a child's drawing into a story, generate visual data based on that story, and quickly provide it as digital content.

[0529] "Image information" refers to visual data expressed in digital format, specifically content represented as pictures or images.

[0530] A "receiving mechanism" is a function that takes in data and information from an external source and converts it into a format that can be used within the system.

[0531] "Feature information" refers to specific elements or attributes extracted from image information, and is used to identify scenes and objects.

[0532] "Analysis means" refers to a device or program that has the function of processing input data and analyzing its components and characteristics.

[0533] A "narrative generation method" is a function for automatically creating text-based stories, constructing content based on extracted feature information.

[0534] "Visual information" refers to visual data generated based on stories or text, and is expressed as illustrations or graphics.

[0535] A "visual generation means" is a device or software for creating visual data based on text information.

[0536] "Display means" refers to a device or program that has the function of showing generated data or information in a form that is recognizable to the user.

[0537] "Organizational methods" refer to the function of combining narrative and visual information and arranging them in the necessary format.

[0538] The system of the present invention includes a terminal used by the user, a server for processing data, and means of communication via the internet. The user uses the terminal to digitize a hand-drawn picture and transmit the image information to the server. The image information is typically acquired using the camera function of a smartphone or tablet.

[0539] The server analyzes the received image information and extracts feature information from it. Image recognition technologies such as the Google Cloud Vision API are used for this analysis. Based on the extracted feature information, the server automatically generates stories using a generative AI model. For example, stories such as "A cat on a spaceship" or "A dog on a pirate ship" are generated.

[0540] Next, the server generates visual information based on the generated story. Models such as DALL-E can be used for this process. The visual information is generated as illustrations corresponding to the story and presented in a user-friendly format.

[0541] The generated stories and visual information are sent to the device, where users can enjoy viewing them. Children, in particular, can experience how their own creative ideas are transformed into digital content.

[0542] As a concrete example, here is an example of a prompt message when a user draws "a dog on a pirate ship".

[0543] Prompt: "The objects drawn are a 'pirate ship' and a 'dog'. Based on these, generate an outline for a children's adventure story."

[0544] This system allows users to easily transform their expressions into digital stories and illustrations and share them with others.

[0545] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0546] Step 1:

[0547] The user uses the device to photograph or scan a hand-drawn picture and saves the image information to the device in digital format. The input is a hand-drawn picture, and the output is digitized image data. This data is prepared for subsequent processing.

[0548] Step 2:

[0549] The terminal transmits the acquired image data to the server. The input is digitized image data, and the output is the transmission of data to the server. The terminal uses a stable communication method to transmit the data.

[0550] Step 3:

[0551] The server analyzes the received image data and extracts feature information using image recognition technology. The input is the transmitted image data, and the output is feature information of objects and scenes within the image. This analysis uses image recognition software (e.g., Google Cloud Vision API).

[0552] Step 4:

[0553] The server uses extracted feature information to generate stories using a generative AI model. The input is feature information, and the output is the text data of the story. Natural language processing techniques are used for story generation, and a story suitable for the content specified by the user is generated.

[0554] Step 5:

[0555] The server generates visual information (illustrations) based on the text data of the story. The input is the text data of the story, and the output is visualized illustration data. In this process, a model (e.g., DALL-E) is used to convert text into visuals.

[0556] Step 6:

[0557] The server combines the generated story and visual information and sends it to the terminal. The input is the story's text data and visual information, and the output is the transmission of data to the terminal. The user receives this data and can view the results on their terminal.

[0558] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0559] This invention relates to a system that recognizes a user's emotions and generates images and text based on those emotions. This system provides a more personalized creative experience by generating stories and illustrations based on drawings created by the user and incorporating the user's emotions.

[0560] Specifically, the user uses their device to capture a drawing they have created, simultaneously sending data for emotion recognition to the server. This process includes methods for acquiring data such as pen pressure and speed during drawing, as well as the user's facial expressions.

[0561] The server analyzes the received image data, extracts features, and then recognizes the user's emotions through an emotion engine. For example, the server can perceive "fun" or "creativity" from the color scheme and dynamic elements of a picture. This emotional information then influences the subsequent automated story generation process.

[0562] When generating a story, the server uses the user's emotional data to create text with a more matching theme and tone. For example, if the user's emotion is recognized as "joy," the story will be adjusted to be positive and upbeat.

[0563] Furthermore, emotional information is also used to generate visual data. In addition to the story content, the server can create illustrations that reflect the user's emotions. For example, if the emotion is "calm," an illustration with a generally soft tone will be generated.

[0564] Finally, the server sends the generated story text and illustrations to the device. The device then organizes them and displays a consistent story and visuals to the user.

[0565] Thus, the present invention is a system that takes user emotions into consideration, thereby providing a more personalized experience and enriching user creativity.

[0566] The following describes the processing flow.

[0567] Step 1:

[0568] The user uses the device to take a picture of or select a drawing they have created and saves it as image data. At the same time, the device collects emotion-related data such as the user's facial expressions, drawing speed, and pen pressure.

[0569] Step 2:

[0570] The device sends image data and emotion data to the server. A secure and fast communication protocol is used for transmission.

[0571] Step 3:

[0572] The server analyzes the received image data and extracts prominent features and objects from the image. Existing image recognition algorithms are applied to this analysis.

[0573] Step 4:

[0574] The server uses an emotion engine to analyze the transmitted emotion data and recognize the user's current emotional state. For example, emotions such as "joy" or "surprise" may be extracted.

[0575] Step 5:

[0576] The server executes a text generation module based on extracted features and the user's emotional state, automatically generating a story. This story is composed of themes and tones that correspond to the user's emotions.

[0577] Step 6:

[0578] The server creates visual data that reflects the user's emotions based on the generated story. Machine learning algorithms are used to adjust the content and colors.

[0579] Step 7:

[0580] The server sends the generated story and visual data to the terminal. The terminal needs to be able to receive and process the data in real time.

[0581] Step 8:

[0582] The device receives data from the server and displays the story and illustrations in a consistent manner on the user interface. This allows users to enjoy creative works that resonate with their emotions.

[0583] (Example 2)

[0584] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0585] In today's world, users demand personalized content based on their emotions. However, traditional technologies have struggled to accurately recognize user emotions and generate narratives and visual content based on them. As a result, they have been unable to provide creative experiences that align with user intentions and have failed to deliver personalized experiences.

[0586] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0587] In this invention, the server includes communication means for receiving image information, analysis means for extracting features and recognizing emotions based on the image information and user emotion recognition information, text generation means for automatically generating text based on the emotion information, and information generation means for generating visual information based on the generated text and emotion information. This makes it possible to appropriately consider the user's emotions and provide personalized stories and visual content.

[0588] "Image information" refers to drawings and other visual data created by users, and is digital data used by computer systems for analysis and processing.

[0589] "Communication methods" refer to the technologies or protocols used to send or receive data from a terminal to a server, and include the internet and local networks.

[0590] "Emotion recognition information" refers to data used as the basis for determining the user's emotional state, and includes pen pressure, drawing speed, and facial expression data.

[0591] "Features" refer to the prominent properties or patterns of the subject being analyzed, obtained as a result of analyzing image information or emotion recognition information.

[0592] "Analysis means" refers to processing techniques that extract features from received image information and emotion recognition information, and understand the data according to a specific purpose.

[0593] "Emotional information" refers to information that identifies a user's emotional state through analytical means, and this information is used to generate narratives and visual content.

[0594] "Text generation means" refers to a technology or algorithm that performs a process of automatically creating text or stories based on the user's emotional information.

[0595] "Visual information" refers to visual content such as images and illustrations created based on the content of the generated story and the user's emotional information.

[0596] "Information generation means" refers to processes and techniques for generating relevant visual information, taking into account emotional information and generated text.

[0597] "Display means" refers to a method or apparatus for presenting the final text and visual information to the user, and includes output devices such as screens and monitors.

[0598] This system aims to recognize the user's emotions based on drawings and illustrations they create, and then generate new stories and visual content from that. First, the user uses a device to scan or photograph their own drawing, capturing it as a digital image. Along with this image data, information for emotion recognition, such as pen pressure and speed data, and the user's facial expression data, is also acquired. This data is transmitted to a server via a wireless network or internet connection.

[0599] The server uses specialized image processing software to analyze the received image data and extract features. This analysis includes evaluating the image's color scheme, shape, and dynamic elements, and using an emotion engine to recognize emotions. Hue histograms and shape recognition algorithms are often used in this process. For example, emotions such as "joy" or "calmness" can be extracted from the color tones of a painting.

[0600] The server then utilizes a generative AI model to automatically generate a story using the user's emotional information as input data. Using natural language processing techniques, the story is created with themes and styles that match the user's emotions. For example, if the user's emotion is "joy," a bright and positive story will be constructed. An example of a prompt might be, "Generate a fun adventure story based on a moment when the user felt joy."

[0601] Furthermore, the server generates visual information based on newly created narratives and emotional information. Using machine learning algorithms, it can generate related illustrations and depict scenes expressed in soft colors. The latest technologies in AI models and image editing software are applied to illustration generation.

[0602] Ultimately, the server sends the generated story text and illustrations back to the device. The device then organizes them appropriately and displays visually and textually consistent content to the user. This allows the user to enjoy an emotionally-driven, personalized creative experience.

[0603] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0604] Step 1:

[0605] The user uses a device to scan or photograph their drawing, capturing it as a digital image. This image data is acquired along with emotion recognition information such as pen pressure and drawing speed. The input is the user's drawing and emotion recognition information, and the output is this dataset. Specific software or applications organize the image data and prepare it for transmission to the server.

[0606] Step 2:

[0607] The terminal transmits acquired image data and emotion recognition information to the server. This transmission is securely performed via the Internet Protocol. The input is data on the terminal, and the output is packet data sent to the server. The transmission is performed using the network connection to ensure data integrity.

[0608] Step 3:

[0609] The server analyzes the received image data using dedicated image processing software and extracts features. The input consists of image data and emotion recognition information, and the processing involves analyzing color usage and shape information. The output is feature data of the image. At this stage, a hue histogram and shape recognition algorithm are used to prepare the system for recognizing the user's emotions from the color tone and shape.

[0610] Step 4:

[0611] The server uses an emotion engine to analyze the user's emotions from identified feature data and generate emotion information. The input is feature data, and the output is emotion information. The emotion engine identifies specified patterns, and as a result, emotions such as "joy" or "sadness" are identified.

[0612] Step 5:

[0613] The server uses a generative AI model to automatically generate stories based on emotional information. The input is emotional information, and the output is the text of the story. Through natural language processing technology, it generates text with themes and tones that match the emotions. This process uses the prompt "Generate a fun adventure story based on the scene where the user felt joy."

[0614] Step 6:

[0615] The server uses the generated story text and emotional information to create corresponding visual information using an AI model. The input is the story text and emotional information, and the output is visual data (illustrations). Specifically, machine learning algorithms generate illustrations, visually representing the story's scenes and characters.

[0616] Step 7:

[0617] The server sends the final generated narrative text and visual information to the terminal. The input is the generated data, and the output is the displayed data sent to the terminal. This data is organized for easy user access and transmitted while maintaining visual consistency.

[0618] Step 8:

[0619] The terminal organizes the received narrative text and visual information and displays it to the user. The input is data received from the server, and the output is the story and illustrations presented to the user. This allows the user to enjoy a unique, emotion-based creative experience.

[0620] (Application Example 2)

[0621] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0622] In today's world, where improving user experience is paramount, there is a growing demand for personalized content that responds to individual emotions. However, generating illustrations and stories that truly reflect user emotions is technically challenging, and this has resulted in a limited user experience.

[0623] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0624] In this invention, the server includes information acquisition means for receiving image data and emotion data; analysis means for extracting features and recognizing emotions based on the image data and emotion data; and text generation means for automatically generating text based on the features and recognized emotions. This enables the generation of personalized text and visual data based on the user's emotions.

[0625] "Image data" refers to digital data that records visual information and is used in analysis and generation processes.

[0626] "Emotional data" refers to data that indicates a user's emotional state, and is information obtained based on the user's facial expressions and behavioral characteristics.

[0627] "Information acquisition means" refers to a function or mechanism for receiving image data and emotional data via a digital device or system.

[0628] "Analysis means" refers to a device or program that uses a process or technology to extract features from received data and recognize emotions.

[0629] "Text generation means" refers to a device or program that uses a process or technology to automatically generate text based on analyzed feature and sentiment data.

[0630] "Data generation means" refers to a device or program that uses a process or technology to create visual data based on generated text and emotional information.

[0631] "Display means" refers to an output device or technology used to provide the generated text and visual data to the user.

[0632] The system for implementing this invention consists of a terminal used by the user and a server in the cloud. First, the user imports image data into the terminal via a smartphone or smart glasses. This allows for the collection of data based on the user's photographs and changes in their emotions. For example, the system extracts the user's facial expression data from a photograph of their face.

[0633] The server receives image data and emotion data using information acquisition means. Here, cloud services such as AWS and Azure are used for data reception and processing. The received data is analyzed by analysis means using image analysis libraries (e.g., OpenCV) to recognize the user's emotional state. Microsoft Azure Emotion API is used for emotion recognition.

[0634] Next, the text generation means automatically generates text using a generative AI model (e.g., OpenAI's GPT) based on the analyzed features and the user's emotions. In the text generation process, prompt sentences are provided, and the generative AI model creates text that matches the emotions.

[0635] Subsequently, the server uses data generation tools to apply machine learning algorithms and sentiment evaluation models to generate visual data based on the generated text and recognized emotions. Generative AI such as DALLE-2 is used to generate the visual data, creating illustrations that align with the user's emotions.

[0636] The generated content is returned to the device via a display device and provided to the user. This allows the user to experience personalized text and art based on their own emotions.

[0637] As a concrete example, when a user takes a photo of autumn leaves, the photo data is input into the system, and the user's smiling expression data is obtained. If the emotion is recognized as "happiness," the following prompt message is input into the AI ​​model to generate a story and art.

[0638] Example of a prompt:

[0639] "The user's photo features an autumn background with a predominantly red and orange color scheme. The user is smiling and appears calm and happy. Please write a story themed around a happy autumn."

[0640] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0641] Step 1:

[0642] The user takes image data using a smartphone and imports it to the device. The input is the captured image data, and the output is a digital image file stored on the device. This file also includes metadata such as the user's current location, time, and shooting conditions.

[0643] Step 2:

[0644] The terminal sends image data it has captured to the server. The server receives the image data using an information acquisition method. The input is image data from the terminal, and the output is the image file transferred to the server. HTTPS is used as the communication protocol.

[0645] Step 3:

[0646] The server analyzes image data and extracts features using an image analysis library (e.g., OpenCV). The input is the received image data, and the output is analyzed feature data such as color and shape. For example, it can identify the main colors in an image and extract them as a data set.

[0647] Step 4:

[0648] The server uses the Microsoft Azure Emotion API to recognize emotions. The input consists of features extracted from image data and data representing the user's facial expressions, while the output is data representing the user's emotional state. This data is labeled with terms such as "joy" or "sadness."

[0649] Step 5:

[0650] The server generates text using a generative AI model (e.g., OpenAI's GPT). The input is emotion data and associated prompt sentences, and the output is the generated text data. The prompt sentences are in the form of "Please write a story about when the user's emotion is XX," and the generative AI model generates an appropriate story.

[0651] Step 6:

[0652] The server generates visual data using data generation methods. The input is generated text and emotion data, and the output is illustration data that corresponds to the emotion. This is a process that visualizes images within text using a generative AI model (e.g., DALLE-2).

[0653] Step 7:

[0654] The server sends generated text and visual data to the terminal. The input is the text and visual data generated on the server, and the output is the content sent to the terminal. This allows the user to see stories and art that resonate with their emotions.

[0655] Step 8:

[0656] The device provides the user with the data it receives through a display mechanism. The input consists of text and visual data received from the server, while the output is customized content displayed on the user's screen. This allows the user to enjoy a personalized experience based on their own emotions.

[0657] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0658] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0659] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0660] [Fourth Embodiment]

[0661] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0662] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0663] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0664] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0665] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0666] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0667] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0668] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0669] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0670] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0671] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0672] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0673] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0674] This invention provides a system that automatically transforms children's drawings and stories into narratives or illustrations. This system is implemented through a series of operations between a server, a terminal, and a user. A specific example is shown below.

[0675] The user uses their device to digitally capture a drawing they have made and uses this as initial data. The image data of the captured drawing is then sent from the device to the server. For example, suppose the user has drawn a picture of "a cat in a spaceship."

[0676] The server analyzes the image data received from the terminal. This analysis uses machine learning-based image recognition technology, and the server extracts features such as objects and scenes contained within the image. In this case, objects such as "cat" and "spaceship" are extracted.

[0677] Next, the server automatically generates a story using a text generation algorithm based on the extracted features. The server generates content such as "A story about a cat traveling through the galaxy on a spaceship" and organizes this text data.

[0678] The server then uses the generated text data of the story to create visual data. In this process, machine learning algorithms are used to draw illustrations that correspond to the story. For example, an illustration is generated depicting a scene of a cat inside a spaceship flying through outer space.

[0679] Finally, the server sends the generated story and illustrations to the device. Upon receiving them, the device displays the story and illustrations in a format that is easy for the user to view. The user can then appreciate the completed story and illustrations on the device screen and enjoy them as a work of art.

[0680] This invention allows users to automatically generate new stories and illustrations through their own creative activities, thus making it easy to give form to their creativity. This specific embodiment embodies all the elements included in the claims and effectively utilizes the technical features of the present invention.

[0681] The following describes the processing flow.

[0682] Step 1:

[0683] The user captures their own drawings as image data using the device's camera or file selection function. This image data is used as input information for the work.

[0684] Step 2:

[0685] The terminal converts the captured image data into the appropriate format and sends it to the server. Internet-based communication is used for transmission.

[0686] Step 3:

[0687] The server receives image data sent from the terminal. The received images are then passed to the image analysis module.

[0688] Step 4:

[0689] The server uses image analysis technology to extract prominent features and objects from the received image data. For example, features such as "cat" and "spaceship" can be recognized from the image.

[0690] Step 5:

[0691] The server automatically generates a story using a text generation module based on the extracted feature information. Natural language processing techniques are used to generate a grammatically correct story.

[0692] Step 6:

[0693] The server passes the generated story text information to the illustration generation module. Here, visual data is generated to represent the scenes in the story.

[0694] Step 7:

[0695] The server uses machine learning algorithms to generate illustrations related to the story. The generated illustrations will be consistent with the content of the story.

[0696] Step 8:

[0697] The server sends the generated story text and illustrations to the terminal. The transmitted data is then prepared to be displayed on the terminal's screen.

[0698] Step 9:

[0699] The terminal displays the story and illustrations received from the server on the user interface. Users can view these and enjoy the generated works.

[0700] (Example 1)

[0701] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0702] There is a need for a system that can easily generate fun stories and engaging illustrations from children's drawings and original stories, thereby further stimulating creative activities. However, current technology lacks the means to do this automatically and efficiently, forcing users to go through a cumbersome process.

[0703] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0704] In this invention, the server includes communication means for receiving image information, automatic generation means for generating a story based on extracted feature elements, and visualization means for generating visual information based on the story. This makes it possible to automatically generate a story and visual data based on images and support the user's creative activities.

[0705] "Image information" refers to information that represents visual data in a digital format, and is usually provided in image file format.

[0706] "Communication means" refers to network interfaces and protocols for sending and receiving data, enabling data transfer between servers and terminals.

[0707] "Analysis" refers to the process of extracting features from received image information, which involves using image recognition algorithms to break down the data and obtain meaningful information.

[0708] A "feature element" refers to a specific attribute or pattern related to an object or scene, extracted from image information.

[0709] "Automatic generation means" refers to a device that has the function of generating content based on characteristic elements, and in particular, generates stories using natural language generation technology.

[0710] A "story" is a structure that includes a series of events and characters described in writing, and it expresses fictional content.

[0711] A "visualization method" is a device that has the function of generating visual data based on text data such as stories, and uses an automated learning algorithm.

[0712] "Visual information" refers to visual representations such as illustrations and shapes generated based on text data.

[0713] A "display means" is an interface that provides a way to present generated narratives and visual information to the user.

[0714] A "terminal" refers to an electronic device that a user can directly operate and use to send and receive data, and includes smartphones, tablets, and other similar devices.

[0715] This invention provides a system that automatically creates stories or illustrations based on drawings or stories created by children. Specific embodiments of this system are described below.

[0716] Users use their devices to digitally capture their own drawings. This is done using the camera or scanner built into their smartphone or tablet. For example, a user might capture a drawing of "a cat in a spaceship." This image is then saved to the device as a digital image file.

[0717] The device sends the saved image data to the server. A secure communication protocol (e.g., HTTPS) is used for transmission via the internet.

[0718] The server utilizes image recognition technology based on machine learning frameworks (e.g., TensorFlow, PyTorch) to analyze the received image data. As a result of the analysis, feature elements such as "cat" or "spaceship" are extracted from the image.

[0719] Next, the server generates a story using natural language processing techniques (e.g., a natural language generation model) based on these feature elements. Specifically, it provides prompt sentences to the generation AI model. An example of a prompt sentence would be, "Describe an adventure in which a cat travels to a new planet in a spaceship and makes friends there."

[0720] Once the story generation is complete, the server then generates visual information. The server uses machine learning algorithms such as DALL-E and Stable Diffusion as means of generating visual data to automatically create illustrations corresponding to each scene of the story.

[0721] The generated story and illustrations are sent from the server to the terminal. The terminal receives this data and uses an appropriate viewer to display the content in a user-friendly format. Users can appreciate the completed story and illustrations and enjoy them as if they were professional works. This entire process allows users to easily bring their creativity to life.

[0722] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0723] Step 1:

[0724] The user uses a device to capture the image in digital format. Specifically, this involves taking a photograph with a smartphone camera or obtaining an image file using a scanner. In this step, the input is a physical image, and the output is a digital image file (e.g., JPEG, PNG).

[0725] Step 2:

[0726] The terminal sends the acquired image file to the server over the network. A secure communication protocol using the internet (e.g., HTTPS) is used for this process. The input is a digital image file, and the output is the image data securely transferred to the server.

[0727] Step 3:

[0728] The server analyzes the received image data. It applies machine learning models using TensorFlow or PyTorch to extract feature elements (e.g., "cat," "spaceship") from the image. The input is digital image data, and the output is a set of feature elements contained in the image.

[0729] Step 4:

[0730] The server automatically generates a story using natural language processing techniques based on the extracted features. The generating AI model is input with a prompt, such as "Describe an adventure where a cat travels to a new planet in a spaceship and makes friends there." The input consists of feature elements and a prompt, and the output is the text data of the generated story.

[0731] Step 5:

[0732] The server generates visual information (illustrations) based on the story's text data, using algorithms such as DALL-E and Stable Diffusion. The input is the story's text data, and the output is the associated illustration data.

[0733] Step 6:

[0734] The server sends the generated story and illustrations to the user's terminal. This transmission process also uses a secure communication protocol. The input is the set of generated story and illustrations, and the output is the data sent to the terminal.

[0735] Step 7:

[0736] The device displays the received story and illustrations. Users can view and enjoy them on the device's display. The input is the story and illustrations received from the server, and the output is the completed work visually presented to the user.

[0737] (Application Example 1)

[0738] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0739] There is a need for a way to easily visualize children's creative activities in narrative and visual form, and to easily share them digitally. Current technology requires manual conversion, which is time-consuming and labor-intensive.

[0740] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0741] In this invention, the server includes receiving means for receiving image information, analysis means for extracting feature information based on the image information, and story generation means for automatically generating a story based on the feature information. This makes it possible to automatically turn a child's drawing into a story, generate visual data based on that story, and quickly provide it as digital content.

[0742] "Image information" refers to visual data expressed in digital format, specifically content represented as pictures or images.

[0743] A "receiving mechanism" is a function that takes in data and information from an external source and converts it into a format that can be used within the system.

[0744] "Feature information" refers to specific elements or attributes extracted from image information, and is used to identify scenes and objects.

[0745] "Analysis means" refers to a device or program that has the function of processing input data and analyzing its components and characteristics.

[0746] A "narrative generation method" is a function for automatically creating text-based stories, constructing content based on extracted feature information.

[0747] "Visual information" refers to visual data generated based on stories or text, and is expressed as illustrations or graphics.

[0748] A "visual generation means" is a device or software for creating visual data based on text information.

[0749] "Display means" refers to a device or program that has the function of showing generated data or information in a form that is recognizable to the user.

[0750] "Organizational methods" refer to the function of combining narrative and visual information and arranging them in the necessary format.

[0751] The system of the present invention includes a terminal used by the user, a server for processing data, and means of communication via the internet. The user uses the terminal to digitize a hand-drawn picture and transmit the image information to the server. The image information is typically acquired using the camera function of a smartphone or tablet.

[0752] The server analyzes the received image information and extracts feature information from it. Image recognition technologies such as the Google Cloud Vision API are used for this analysis. Based on the extracted feature information, the server automatically generates stories using a generative AI model. For example, stories such as "A cat on a spaceship" or "A dog on a pirate ship" are generated.

[0753] Next, the server generates visual information based on the generated story. Models such as DALL-E can be used for this process. The visual information is generated as illustrations corresponding to the story and presented in a user-friendly format.

[0754] The generated stories and visual information are sent to the device, where users can enjoy viewing them. Children, in particular, can experience how their own creative ideas are transformed into digital content.

[0755] As a concrete example, here is an example of a prompt message when a user draws "a dog on a pirate ship".

[0756] Prompt: "The objects drawn are a 'pirate ship' and a 'dog'. Based on these, generate an outline for a children's adventure story."

[0757] This system allows users to easily transform their expressions into digital stories and illustrations and share them with others.

[0758] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0759] Step 1:

[0760] The user uses the device to photograph or scan a hand-drawn picture and saves the image information to the device in digital format. The input is a hand-drawn picture, and the output is digitized image data. This data is prepared for subsequent processing.

[0761] Step 2:

[0762] The terminal transmits the acquired image data to the server. The input is digitized image data, and the output is the transmission of data to the server. The terminal uses a stable communication method to transmit the data.

[0763] Step 3:

[0764] The server analyzes the received image data and extracts feature information using image recognition technology. The input is the transmitted image data, and the output is feature information of objects and scenes within the image. This analysis uses image recognition software (e.g., Google Cloud Vision API).

[0765] Step 4:

[0766] The server uses extracted feature information to generate stories using a generative AI model. The input is feature information, and the output is the text data of the story. Natural language processing techniques are used for story generation, and a story suitable for the content specified by the user is generated.

[0767] Step 5:

[0768] The server generates visual information (illustrations) based on the text data of the story. The input is the text data of the story, and the output is visualized illustration data. In this process, a model (e.g., DALL-E) is used to convert text into visuals.

[0769] Step 6:

[0770] The server combines the generated story and visual information and sends it to the terminal. The input is the story's text data and visual information, and the output is the transmission of data to the terminal. The user receives this data and can view the results on their terminal.

[0771] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0772] This invention relates to a system that recognizes a user's emotions and generates images and text based on those emotions. This system provides a more personalized creative experience by generating stories and illustrations based on drawings created by the user and incorporating the user's emotions.

[0773] Specifically, the user uses their device to capture a drawing they have created, simultaneously sending data for emotion recognition to the server. This process includes methods for acquiring data such as pen pressure and speed during drawing, as well as the user's facial expressions.

[0774] The server analyzes the received image data, extracts features, and then recognizes the user's emotions through an emotion engine. For example, the server can perceive "fun" or "creativity" from the color scheme and dynamic elements of a picture. This emotional information then influences the subsequent automated story generation process.

[0775] When generating a story, the server uses the user's emotional data to create text with a more matching theme and tone. For example, if the user's emotion is recognized as "joy," the story will be adjusted to be positive and upbeat.

[0776] Furthermore, emotional information is also used to generate visual data. In addition to the story content, the server can create illustrations that reflect the user's emotions. For example, if the emotion is "calm," an illustration with a generally soft tone will be generated.

[0777] Finally, the server sends the generated story text and illustrations to the device. The device then organizes them and displays a consistent story and visuals to the user.

[0778] Thus, the present invention is a system that takes user emotions into consideration, thereby providing a more personalized experience and enriching user creativity.

[0779] The following describes the processing flow.

[0780] Step 1:

[0781] The user uses the device to take a picture of or select a drawing they have created and saves it as image data. At the same time, the device collects emotion-related data such as the user's facial expressions, drawing speed, and pen pressure.

[0782] Step 2:

[0783] The device sends image data and emotion data to the server. A secure and fast communication protocol is used for transmission.

[0784] Step 3:

[0785] The server analyzes the received image data and extracts prominent features and objects from the image. Existing image recognition algorithms are applied to this analysis.

[0786] Step 4:

[0787] The server uses an emotion engine to analyze the transmitted emotion data and recognize the user's current emotional state. For example, emotions such as "joy" or "surprise" may be extracted.

[0788] Step 5:

[0789] The server executes a text generation module based on extracted features and the user's emotional state, automatically generating a story. This story is composed of themes and tones that correspond to the user's emotions.

[0790] Step 6:

[0791] The server creates visual data that reflects the user's emotions based on the generated story. Machine learning algorithms are used to adjust the content and colors.

[0792] Step 7:

[0793] The server sends the generated story and visual data to the terminal. The terminal needs to be able to receive and process the data in real time.

[0794] Step 8:

[0795] The device receives data from the server and displays the story and illustrations in a consistent manner on the user interface. This allows users to enjoy creative works that resonate with their emotions.

[0796] (Example 2)

[0797] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0798] In today's world, users demand personalized content based on their emotions. However, traditional technologies have struggled to accurately recognize user emotions and generate narratives and visual content based on them. As a result, they have been unable to provide creative experiences that align with user intentions and have failed to deliver personalized experiences.

[0799] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0800] In this invention, the server includes communication means for receiving image information, analysis means for extracting features and recognizing emotions based on the image information and user emotion recognition information, text generation means for automatically generating text based on the emotion information, and information generation means for generating visual information based on the generated text and emotion information. This makes it possible to appropriately consider the user's emotions and provide personalized stories and visual content.

[0801] "Image information" refers to drawings and other visual data created by users, and is digital data used by computer systems for analysis and processing.

[0802] "Communication methods" refer to the technologies or protocols used to send or receive data from a terminal to a server, and include the internet and local networks.

[0803] "Emotion recognition information" refers to data used as the basis for determining the user's emotional state, and includes pen pressure, drawing speed, and facial expression data.

[0804] "Features" refer to the prominent properties or patterns of the subject being analyzed, obtained as a result of analyzing image information or emotion recognition information.

[0805] "Analysis means" refers to processing techniques that extract features from received image information and emotion recognition information, and understand the data according to a specific purpose.

[0806] "Emotional information" refers to information that identifies a user's emotional state through analytical means, and this information is used to generate narratives and visual content.

[0807] "Text generation means" refers to a technology or algorithm that performs a process of automatically creating text or stories based on the user's emotional information.

[0808] "Visual information" refers to visual content such as images and illustrations created based on the content of the generated story and the user's emotional information.

[0809] "Information generation means" refers to processes and techniques for generating relevant visual information, taking into account emotional information and generated text.

[0810] "Display means" refers to a method or apparatus for presenting the final text and visual information to the user, and includes output devices such as screens and monitors.

[0811] This system aims to recognize the user's emotions based on drawings and illustrations they create, and then generate new stories and visual content from that. First, the user uses a device to scan or photograph their own drawing, capturing it as a digital image. Along with this image data, information for emotion recognition, such as pen pressure and speed data, and the user's facial expression data, is also acquired. This data is transmitted to a server via a wireless network or internet connection.

[0812] The server uses specialized image processing software to analyze the received image data and extract features. This analysis includes evaluating the image's color scheme, shape, and dynamic elements, and using an emotion engine to recognize emotions. Hue histograms and shape recognition algorithms are often used in this process. For example, emotions such as "joy" or "calmness" can be extracted from the color tones of a painting.

[0813] The server then utilizes a generative AI model to automatically generate a story using the user's emotional information as input data. Using natural language processing techniques, the story is created with themes and styles that match the user's emotions. For example, if the user's emotion is "joy," a bright and positive story will be constructed. An example of a prompt might be, "Generate a fun adventure story based on a moment when the user felt joy."

[0814] Furthermore, the server generates visual information based on newly created narratives and emotional information. Using machine learning algorithms, it can generate related illustrations and depict scenes expressed in soft colors. The latest technologies in AI models and image editing software are applied to illustration generation.

[0815] Ultimately, the server sends the generated story text and illustrations back to the device. The device then organizes them appropriately and displays visually and textually consistent content to the user. This allows the user to enjoy an emotionally-driven, personalized creative experience.

[0816] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0817] Step 1:

[0818] The user uses a device to scan or photograph their drawing, capturing it as a digital image. This image data is acquired along with emotion recognition information such as pen pressure and drawing speed. The input is the user's drawing and emotion recognition information, and the output is this dataset. Specific software or applications organize the image data and prepare it for transmission to the server.

[0819] Step 2:

[0820] The terminal transmits acquired image data and emotion recognition information to the server. This transmission is securely performed via the Internet Protocol. The input is data on the terminal, and the output is packet data sent to the server. The transmission is performed using the network connection to ensure data integrity.

[0821] Step 3:

[0822] The server analyzes the received image data using dedicated image processing software and extracts features. The input consists of image data and emotion recognition information, and the processing involves analyzing color usage and shape information. The output is feature data of the image. At this stage, a hue histogram and shape recognition algorithm are used to prepare the system for recognizing the user's emotions from the color tone and shape.

[0823] Step 4:

[0824] The server uses an emotion engine to analyze the user's emotions from identified feature data and generate emotion information. The input is feature data, and the output is emotion information. The emotion engine identifies specified patterns, and as a result, emotions such as "joy" or "sadness" are identified.

[0825] Step 5:

[0826] The server uses a generative AI model to automatically generate stories based on emotional information. The input is emotional information, and the output is the text of the story. Through natural language processing technology, it generates text with themes and tones that match the emotions. This process uses the prompt "Generate a fun adventure story based on the scene where the user felt joy."

[0827] Step 6:

[0828] The server uses the generated story text and emotional information to create corresponding visual information using an AI model. The input is the story text and emotional information, and the output is visual data (illustrations). Specifically, machine learning algorithms generate illustrations, visually representing the story's scenes and characters.

[0829] Step 7:

[0830] The server sends the final generated narrative text and visual information to the terminal. The input is the generated data, and the output is the displayed data sent to the terminal. This data is organized for easy user access and transmitted while maintaining visual consistency.

[0831] Step 8:

[0832] The terminal organizes the received narrative text and visual information and displays it to the user. The input is data received from the server, and the output is the story and illustrations presented to the user. This allows the user to enjoy a unique, emotion-based creative experience.

[0833] (Application Example 2)

[0834] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0835] In today's world, where improving user experience is paramount, there is a growing demand for personalized content that responds to individual emotions. However, generating illustrations and stories that truly reflect user emotions is technically challenging, and this has resulted in a limited user experience.

[0836] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0837] In this invention, the server includes information acquisition means for receiving image data and emotion data; analysis means for extracting features and recognizing emotions based on the image data and emotion data; and text generation means for automatically generating text based on the features and recognized emotions. This enables the generation of personalized text and visual data based on the user's emotions.

[0838] "Image data" refers to digital data that records visual information and is used in analysis and generation processes.

[0839] "Emotional data" refers to data that indicates a user's emotional state, and is information obtained based on the user's facial expressions and behavioral characteristics.

[0840] "Information acquisition means" refers to a function or mechanism for receiving image data and emotional data via a digital device or system.

[0841] "Analysis means" refers to a device or program that uses a process or technology to extract features from received data and recognize emotions.

[0842] "Text generation means" refers to a device or program that uses a process or technology to automatically generate text based on analyzed feature and sentiment data.

[0843] "Data generation means" refers to a device or program that uses a process or technology to create visual data based on generated text and emotional information.

[0844] "Display means" refers to an output device or technology used to provide the generated text and visual data to the user.

[0845] The system for implementing this invention consists of a terminal used by the user and a server in the cloud. First, the user imports image data into the terminal via a smartphone or smart glasses. This allows for the collection of data based on the user's photographs and changes in their emotions. For example, the system extracts the user's facial expression data from a photograph of their face.

[0846] The server receives image data and emotion data using information acquisition means. Here, cloud services such as AWS and Azure are used for data reception and processing. The received data is analyzed by analysis means using image analysis libraries (e.g., OpenCV) to recognize the user's emotional state. Microsoft Azure Emotion API is used for emotion recognition.

[0847] Next, the text generation means automatically generates text using a generative AI model (e.g., OpenAI's GPT) based on the analyzed features and the user's emotions. In the text generation process, prompt sentences are provided, and the generative AI model creates text that matches the emotions.

[0848] Subsequently, the server uses data generation tools to apply machine learning algorithms and sentiment evaluation models to generate visual data based on the generated text and recognized emotions. Generative AI such as DALLE-2 is used to generate the visual data, creating illustrations that align with the user's emotions.

[0849] The generated content is returned to the device via a display device and provided to the user. This allows the user to experience personalized text and art based on their own emotions.

[0850] As a concrete example, when a user takes a photo of autumn leaves, the photo data is input into the system, and the user's smiling expression data is obtained. If the emotion is recognized as "happiness," the following prompt message is input into the AI ​​model to generate a story and art.

[0851] Example of a prompt:

[0852] "The user's photo features an autumn background with a predominantly red and orange color scheme. The user is smiling and appears calm and happy. Please write a story themed around a happy autumn."

[0853] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0854] Step 1:

[0855] The user takes image data using a smartphone and imports it to the device. The input is the captured image data, and the output is a digital image file stored on the device. This file also includes metadata such as the user's current location, time, and shooting conditions.

[0856] Step 2:

[0857] The terminal sends image data it has captured to the server. The server receives the image data using an information acquisition method. The input is image data from the terminal, and the output is the image file transferred to the server. HTTPS is used as the communication protocol.

[0858] Step 3:

[0859] The server analyzes image data and extracts features using an image analysis library (e.g., OpenCV). The input is the received image data, and the output is analyzed feature data such as color and shape. For example, it can identify the main colors in an image and extract them as a data set.

[0860] Step 4:

[0861] The server uses the Microsoft Azure Emotion API to recognize emotions. The input consists of features extracted from image data and data representing the user's facial expressions, while the output is data representing the user's emotional state. This data is labeled with terms such as "joy" or "sadness."

[0862] Step 5:

[0863] The server generates text using a generative AI model (e.g., OpenAI's GPT). The input is emotion data and associated prompt sentences, and the output is the generated text data. The prompt sentences are in the form of "Please write a story about when the user's emotion is XX," and the generative AI model generates an appropriate story.

[0864] Step 6:

[0865] The server generates visual data using data generation methods. The input is generated text and emotion data, and the output is illustration data that corresponds to the emotion. This is a process that visualizes images within text using a generative AI model (e.g., DALLE-2).

[0866] Step 7:

[0867] The server sends generated text and visual data to the terminal. The input is the text and visual data generated on the server, and the output is the content sent to the terminal. This allows the user to see stories and art that resonate with their emotions.

[0868] Step 8:

[0869] The device provides the user with the data it receives through a display mechanism. The input consists of text and visual data received from the server, while the output is customized content displayed on the user's screen. This allows the user to enjoy a personalized experience based on their own emotions.

[0870] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0871] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0872] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0873] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0874] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0875] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0876] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0877] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0878] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0879] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0880] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0881] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0882] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0883] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0884] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0885] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0886] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0887] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0888] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0889] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0890] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0891] The following is further disclosed regarding the embodiments described above.

[0892] (Claim 1)

[0893] A means of receiving image data,

[0894] An analysis means for extracting features based on the aforementioned image data,

[0895] A text generation means that automatically generates text based on the aforementioned features,

[0896] A data generation means that receives the aforementioned text and generates visual data based on the text,

[0897] A display means for outputting the generated text and visual data,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, wherein the text generation means uses natural language processing technology.

[0901] (Claim 3)

[0902] The system according to claim 1, wherein the data generation means utilizes a machine learning algorithm.

[0903] "Example 1"

[0904] (Claim 1)

[0905] A communication means for receiving image information,

[0906] An automatic generation means that analyzes the aforementioned image information and generates a story based on the extracted feature elements,

[0907] A visualization means for generating visual information based on the aforementioned story,

[0908] A display means that transmits the generated story and visual information to a terminal and displays it,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, wherein the automatic generation means uses natural language processing technology to form a story.

[0912] (Claim 3)

[0913] The system according to claim 1, wherein the visualization means uses an automatic learning algorithm.

[0914] "Application Example 1"

[0915] (Claim 1)

[0916] A receiving means for receiving image information,

[0917] An analysis means for extracting feature information based on the aforementioned image information,

[0918] A story generation means that automatically generates a story based on the aforementioned feature information,

[0919] A visual generation means that receives the aforementioned story and generates visual information based on that story,

[0920] A display means for outputting the generated story and visual information,

[0921] A method for compiling visualized content by combining generative narratives and visual information,

[0922] A system that includes this.

[0923] (Claim 2)

[0924] The system according to claim 1, wherein the story generation means uses natural language processing technology.

[0925] (Claim 3)

[0926] The system according to claim 1, wherein the visual generation means utilizes a machine learning algorithm.

[0927] "Example 2 of combining an emotion engine"

[0928] (Claim 1)

[0929] A communication means for receiving image information,

[0930] An analysis means for extracting features and recognizing emotions based on the aforementioned image information and user emotion recognition information,

[0931] A text generation means that automatically generates text based on the aforementioned sentiment information,

[0932] Information generation means for generating visual information based on the generated text and emotional information,

[0933] A display means for outputting the generated text and visual information,

[0934] A system that includes this.

[0935] (Claim 2)

[0936] The system according to claim 1, wherein the text generation means uses natural language processing technology to generate text that matches the user's emotions.

[0937] (Claim 3)

[0938] The system according to claim 1, wherein the information generation means utilizes a machine learning algorithm to generate visual information that reflects the user's emotions.

[0939] "Application example 2 when combining with an emotional engine"

[0940] (Claim 1)

[0941] Information acquisition means for receiving image data and emotion data,

[0942] An analysis means for extracting features and recognizing emotions based on the aforementioned image data and emotion data,

[0943] A text generation means that automatically generates text based on the aforementioned characteristics and recognized emotions,

[0944] A data generation means that generates visual data taking into account the aforementioned text and emotions,

[0945] A display means for outputting the generated text and visual data,

[0946] A system that includes this.

[0947] (Claim 2)

[0948] The system according to claim 1, wherein the text generation means uses natural language processing technology and generates prompt text using a generation AI model.

[0949] (Claim 3)

[0950] The system according to claim 1, wherein the data generation means utilizes a machine learning algorithm and an emotion evaluation model. [Explanation of Symbols]

[0951] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving image data, An analysis means for extracting features based on the aforementioned image data, A text generation means that automatically generates text based on the aforementioned features, A data generation means that receives the aforementioned text and generates visual data based on the text, A display means for outputting the generated text and visual data, A system that includes this.

2. The system according to claim 1, wherein the text generation means uses natural language processing technology.

3. The system according to claim 1, wherein the data generation means utilizes a machine learning algorithm.

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